REGULARIZATION METHODS FOR ESTIMATING A MULTI-FACTOR CORPORATE BOND PRICING MODEL: AN APPLICATION FOR BRAZIL

2021 ◽  
pp. 2150005
Author(s):  
PAULO ROBERTO GUIMARÃES ◽  
OSVALDO CANDIDO ◽  
ANDRÉ RONZANI
Keyword(s):  
Mean Squared Error ◽  
Predictive Performance ◽  
Least Square ◽  
Corporate Bond ◽  
Ridge Estimator ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
Optimal Value ◽  
Selection Operator ◽  
Public Offering

The present work focused on studying which factors affect Brazilian inflation-linked corporate bond prices in a primary market setting. The explanatory variables tested were rating, maturity, duration, issuer governance level, industrial classification, collateral, tax exemption, public offering modality, financial volume, coupon frequency, number of issues, number of days since going public, and the Brazilian basic interest rate target. In order to choose the set of variables with best predictive performance, best subsets ordinary least square (OLS) and least absolute shrinkage and selection operator (LASSO) were applied on a testing sample. For estimating purposes, we also tested the Ridge estimator. For both LASSO and Ridge, we used the k-fold approach to choose the optimal value for the lambda penalty. In terms of smallest mean squared error, the OLS estimator outperformed both the Ridge and the LASSO. This result suggests that the variance-bias trade-off might not be a concern for the Brazilian case.

scholarly journals Statistical Analysis of Nigeria’s Price Sector: An Econometric Approach

2021 ◽  
pp. 29-40
Author(s):  
D. M. O. Omebo ◽  
T. D. Ailobhio ◽  
G. I. Fanen
Keyword(s):  
Predictive Performance ◽  
Least Square Method ◽  
Least Square ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
First Order ◽  
Overall Performance ◽  
Econometric Approach ◽  
Good Predictive Performance ◽  
High Measure

This study analyzed Nigeria’s price sector using a formulated model for the price sector of the Nigeria economy. A set of simultaneous equations were used to reflect the implicit gross domestic product deflators for each of the sectors of the Nigeria economy and was found to be over identified under the order condition for identification. The model was estimated by ordinary least square method and two stage least square methods. All the variables have expected signs and as indicated by the F –statistic, the overall performance of the entire regression is significant.  The high measure of R2 and Ṝ2, in each case indicates that the explanatory variables included in the equation jointly account for the entire variation. The small RMSE also indicates that the equations have good fit. Durbin –Watson statistics shows that there is no positive first order autocorrelation. The small value of the Theil’s inequality indicates that the equation has good predictive performance. The researcher therefore recommends that government should employ the model so as to be able to monitor price of each of the sectors of the economy and put proper mechanism in place to control those sectors that affect the overall price sector of the economy.

scholarly journals Estimating a global demand model for soybean traffic through the Panama Canal

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Javier Ho ◽  
Paul Bernal
Keyword(s):  
Pacific Northwest ◽  
Least Square ◽  
Panama Canal ◽  
Demand Model ◽  
Department Of Agriculture ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
Global Demand ◽  
Alternative Routes ◽  
The U.S

AbstractThis study attempts to fit a global demand model for soybean traffic through the Panama Canal using Ordinary Least Square. Most of the soybean cargo through the interoceanic waterway is loaded on the U.S. Gulf and East Coast ports -mainly destined to East Asia, especially China-, and represented about 34% of total Panama Canal grain traffic between fiscal years 2010–19. To estimate the global demand model for soybean traffic, we are considering explanatory variables such as effective toll rates through the Panama Canal, U.S. Gulf- Asia and U.S. Pacific Northwest- Asia freight rates, Baltic Dry Index, bunker costs, soybean export inspections from the U.S. Gulf and Pacific Northwest, U.S. Gulf soybean basis levels, Brazil’s soybean exports and average U.S. dollar index. As part of the research, we are pursuing the estimation of the toll rate elasticity of vessels transporting soybeans via the Panama Canal. Data come mostly from several U.S. Department of Agriculture sources, Brazil’s Secretariat of Foreign Trade (SECEX) and from Panama Canal transit information. Finally, after estimation of the global demand model for soybean traffic, we will discuss the implications for future soybean traffic through the waterway, evaluating alternative routes and sources for this trade.

scholarly journals The Factors Affecting Land Prices In Housing Location In Sidoarjo Regency

2017 ◽  
Vol 1 (1) ◽  
pp. 37-47
Author(s):  
Partomi Simangunsong ◽  
Arasy Alimudin ◽  
Muh. Barid Nizaruddin Wajdi
Keyword(s):  
Residential Location ◽  
Least Square ◽  
City Center ◽  
Sale Price ◽  
Factors Affecting ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
Road Width ◽  
Log Linear ◽  
Road Distance

The need for residential location is one of the basic needs of the community and the attractiveness of the residential location is a unique feature where this feature is not made by the respective occupants, but by external factors from the residential environment in the area. This study aims to analyze the factors that are considered as the basis that affect the price of land. This research uses quantitative approach with associative research method. Linear analysis with quadratic method. Ordinary Least Square (OLS). From the analysis of this research model obtained log-linear F-accounting 70,162 while the value of F-table (0,05; 5,48) is 2,45. because F-count> F-table, Ho means rejected and explanatory variables include Distance to city center, Distance to main road, Distance to toll gate, Road width, and security simultaneously can be explained significantly at land sale price.

scholarly journals Determinants of Household Debt in Malaysia from the year 2010 to 2017

2019 ◽  
Vol 5 (2) ◽  
pp. 91
Author(s):  
Zahariah Mohd Zain ◽  
Nurul Ainun Ahmad Atory Ahmad Atory ◽  
Sarah Amirah Hanafi
Keyword(s):  
Positive Relationship ◽  
Negative Relationship ◽  
Housing Price ◽  
Least Square ◽  
Household Debt ◽  
Macroeconomic Variables ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
Independent Variables ◽  
Lending Rate

Household debt has become an issue in the Malaysian economy as it affects the country socially and economically.This study aims to examine the determinants of household debt from the year 2010 until 2017. This study employs the Ordinary Least Square (OLS) method and the macroeconomic variables used in this study are Gross Domestic Product (GDP), base lending rate, unemployment and housing price as independent variables. The results indicate that the trend of household debt in Malaysia has shown a continuous rise from the year 2010 to 2017. GDP, base lending rate and housing price indicate a positive relationship towards household debt while unemployment shows a negative relationship to household debt in Malaysia. All explanatory variables have shown a significant relationship except for GDP. Housing price has been found to be the most significant factor and positively related to household debt. The findings indicate that the higher the price of houses, the higher the household debt will be.

scholarly journals Employing particle swarm optimization algorithm for shrinkage parameter estimation in generalized Liu estimator

2020 ◽  
Vol 8 (1) ◽  
pp. 10
Author(s):  
Qamar Abdulkareem Abdulazeez ◽  
Zakariya Yahya Algamal
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Method ◽  
Least Square ◽  
Superior Performance ◽  
Ridge Estimator ◽  
Swarm Optimization ◽  
Liu Estimator ◽  
Ordinary Least Square ◽  
Parameter Matrix

It is well-known that in the presence of multicollinearity, the Liu estimator is an alternative to the ordinary least square (OLS) estimator and the ridge estimator. Generalized Liu estimator (GLE) is a generalization of the Liu estimator. However, the efficiency of GLE depends on appropriately choosing the shrinkage parameter matrix which is involved in the GLE. In this paper, a particle swarm optimization method, which is a metaheuristic continuous algorithm, is proposed to estimate the shrinkage parameter matrix. The simulation study and real application results show the superior performance of the proposed method in terms of prediction error.   

scholarly journals KINERJA JACKKNIFE RIDGE REGRESSION DALAM MENGATASI MULTIKOLINEARITAS

2014 ◽  
Vol 3 (4) ◽  
pp. 146
Author(s):  
HANY DEVITA ◽  
I KOMANG GDE SUKARSA ◽  
I PUTU EKA N. KENCANA
Keyword(s):  
Linear Correlation ◽  
Ridge Regression ◽  
Minimum Variance ◽  
Sum Of Squares ◽  
Least Square ◽  
Bias Parameter ◽  
Ridge Estimator ◽  
Ordinary Least Square ◽  
Parameter Estimations

Ordinary least square is a parameter estimations for minimizing residual sum of squares. If the multicollinearity was found in the data, unbias estimator with minimum variance could not be reached. Multicollinearity is a linear correlation between independent variabels in model. Jackknife Ridge Regression(JRR) as an extension of Generalized Ridge Regression (GRR) for solving multicollinearity.  Generalized Ridge Regression is used to overcome the bias of estimators caused of presents multicollinearity by adding different bias parameter for each independent variabel in least square equation after transforming the data into an orthoghonal form. Beside that, JRR can  reduce the bias of the ridge estimator. The result showed that JRR model out performs GRR model.

scholarly journals A Data-Driven Approach to Trip Generation Modeling for Urban Residents and Non-local Travelers

2020 ◽  
Vol 12 (18) ◽  
pp. 7688
Author(s):  
Fan Yang ◽  
Linchao Li ◽  
Fan Ding ◽  
Huachun Tan ◽  
Bin Ran
Keyword(s):  
Cellular Phone ◽  
Test Methods ◽  
Least Square ◽  
Data Driven ◽  
Urban Residents ◽  
Trip Generation ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
Data Driven Approach ◽  
Non Local

Trip generation modeling is essential in transportation planning activities. Previous modeling methods that depend on traditional data collection methods are inefficient and expensive. This paper proposed a novel data-driven trip generation modeling method for urban residents and non-local travelers utilizing location-based social network (LBSN) data and cellular phone data and conducted a case study in Nanjing, China. First, the point of interest (POI) data of the LBSN were classified into various categories by the service type, then, four features of each category including the number of users, number of POIs, number of check-ins, and number of photos were aggregated by traffic analysis zones to be used as explanatory variables for the trip generation models. We used a random tree regression method to select the most important features as the model inputs, and the trip models were established based on the ordinary least square model. Then, an exploratory approach was used to test the performance of each combination of the variables with various test methods to identify the best model for residents’ and travelers’ trip generation functions. The results suggest land use compositions have significant impact on trip generations, and the trip generation patterns are different between urban residents and non-local travelers.

scholarly journals Solving Multicollinearity Problem in Linear Regression Model: The Review Suggests New Idea of Partitioning and Extraction of the Explanatory Variables

2021 ◽  
Vol 2 (1) ◽  
pp. 12-20
Author(s):  
Kayode Ayinde, Olusegun O. Alabi ◽  
Ugochinyere Ihuoma Nwosu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Real Data ◽  
Least Square ◽  
Model Parameters ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
Wide Range ◽  
High Level

Multicollinearity has remained a major problem in regression analysis and should be sustainably addressed. Problems associated with multicollinearity are worse when it occurs at high level among regressors. This review revealed that studies on the subject have focused on developing estimators regardless of effect of differences in levels of multicollinearity among regressors. Studies have considered single-estimator and combined-estimator approaches without sustainable solution to multicollinearity problems. The possible influence of partitioning the regressors according to multicollinearity levels and extracting from each group to develop estimators that will estimate the parameters of a linear regression model when multicollinearity occurs is a new econometrics idea and therefore requires attention. The results of new studies should be compared with existing methods namely principal components estimator, partial least squares estimator, ridge regression estimator and the ordinary least square estimators using wide range of criteria by ranking their performances at each level of multicollinearity parameter and sample size. Based on a recent clue in literature, it is possible to develop innovative estimator that will sustainably solve the problem of multicollinearity through partitioning and extraction of explanatory variables approaches and identify situations where the innovative estimator will produce most efficient result of the model parameters. The new estimator should be applied to real data and popularized for use.

scholarly journals The Effect of Foreign Direct Investment on the Unemployment Rate in Saudi Arabia

2020 ◽  
Vol 12 (10) ◽  
pp. 1
Author(s):  
Kolthoom Alkofahi
Keyword(s):  
Saudi Arabia ◽  
Foreign Direct Investment ◽  
Unemployment Rate ◽  
Direct Investment ◽  
Least Square ◽  
Total Output ◽  
Kingdom Of Saudi Arabia ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
Job Opportunities

A substantial number of recent studies were devoted to investigating the effects of Foreign direct investment (FDI) on different economic variables. Although the connection between growth and investments is widely acknowledged, the connection between FDI and the unemployment rate is not easy to determine. Taking into consideration the dispute over the true effect of FDI on the host country’s economic performance, the study’s main purpose is to take advantage of the dispute and study the effect of foreign direct investment (FDI) on the unemployment rate (U) in the Kingdom of Saudi Arabia (KSA). Using Ordinary Least Square Model (OLS), the study takes the unemployment rate as a dependent variable, and FDI and Output as two explanatory variables over the period of 2005-2018. The study supports our assumption that the inflows of the FDI and the total output negatively and significantly affect the unemployment rate in the KSA; the inflows of the FDI creates more job opportunities and will reduce the unemployment rate in KSA. Our recommendation is that the KSA government should implement more policies to attract more inflows of “Quality FDI” to attain the maximum goals and to decrease the total unemployment rate.

scholarly journals Modified Robust Ridge M-Estimators in Two-Parameter Ridge Regression Model

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Seyab Yasin ◽  
Sultan Salem ◽  
Hamdi Ayed ◽  
Shahid Kamal ◽  
Muhammad Suhail ◽  
...  
Keyword(s):  
Ridge Regression ◽  
Mean Squared Error ◽  
Least Square ◽  
Ridge Estimator ◽  
Least Square Estimator ◽  
Squared Error ◽  
Joint Problem ◽  
Ridge Regression Estimator ◽  
Simulation Results ◽  
Two Parameter

The methods of two-parameter ridge and ordinary ridge regression are very sensitive to the presence of the joint problem of multicollinearity and outliers in the y-direction. To overcome this problem, modified robust ridge M-estimators are proposed. The new estimators are then compared with the existing ones by means of extensive Monte Carlo simulations. According to mean squared error (MSE) criterion, the new estimators outperform the least square estimator, ridge regression estimator, and two-parameter ridge estimator in many considered scenarios. Two numerical examples are also presented to illustrate the simulation results.

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